اسلاید پاورپوینت: غروب کردن (set) ، نمونه برداشتن (sample) و فروش (sale)…

 

عناوین اصلی استخراج شده از این فایل پاورپوینت

عناوین اصلی استخراج شده از این فایل پاورپوینت

● Introduction to Big Data
& Basic Data Analysis
● Big Data EveryWhere!
● How much data?
● The Earthscope
● Type of Data
● What to do with these data?
● Statistics 101
● Random Sample and Statistics
● Statistic
● Empirical Cumulative Distribution Function
● Example
● Measures of Central Tendency (Mean)
● Measures of Central Tendency (Median)
● Example
● Measures of Dispersion (Range)
● Measures of Dispersion (Inter-Quartile Range)
● Measures of Dispersion
(Variance and Standard Deviation)
● Univariate Normal Distribution
● Multivariate Normal Distribution
● OLAP and Data Mining
● Warehouse Architecture
● Star Schemas
● Terms
● Star
● Cube
● ۳-D Cube
● ROLAP vs. MOLAP
● Aggregates
● Another Example
● Aggregates
● What is Data Mining?
● Data Mining Tasks
● Classification: Definition
● Decision Trees
● Clustering
● K-Means Clustering
● Association Rule Mining
● Association Rule Discovery
● Collaborative Filtering
● Other Types of Mining
● Data Streams
● A Simple Problem
● KRP algorithm
─ Karp, et. al (TODS’ ۰۳)
● Streaming Sample Problem

نوع زبان : انگلیسی حجم : ۲٫۴۴ مگا بایت
نوع فایل : اسلاید پاورپوینت تعداد اسلایدها: ۴۷ صفحه
زمان استخراج مطلب : ۲۰۱۸/۱۱/۰۲ ۰۲:۲۷:۴۹ پسوند فایل : pptx

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این مطلب در تاریخ ۲۰۱۸/۱۱/۰۲ ۰۲:۲۷:۴۹ به صورت خودکار استخراج شده است. در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. همچنین این مطلب برگرفته از وب سایت زیر است و مسئولیت انتشار آن با منبع اصلی است.

http://www.cs.kent.edu/~jin/Cloud12Spring/BigData.pptx

بخشی از محتوای متن استخراج شده از این فایل ppt

بخشی از محتوای متن استخراج شده از این فایل ppt

introduction to big data basic data analysis ۱ big data everywhere lots of data is being collected and warehoused web data e commerce purchases at department grocery stores bank credit card transactions social network ۲ how much data google processes ۲ pb a day ۲ ۸ wayback machine has ۳ pb ۱ tb month ۳ ۲ ۹ facebook has ۲.۵ pb of user data ۱۵ tb day ۴ ۲ ۹ ebay has ۶.۵ pb of user data ۵ tb day ۵ ۲ ۹ cern’s large hydron collider lhc generates ۱۵ pb a year ۶۴ k ought to be enough for anybody. maximilien brice cern the earthscope the earthscope is the world s largest science project. designed to track north america s geological evolution this observatory records data over ۳.۸ million square miles amassing ۶۷ terabytes of data. it analyzes seismic slips in the san andreas fault sure but also the plume of magma underneath yellowstone and much much more. http www.msnbc.msn.com id ۴۴۳۶۳۵۹۸ ns technology and science future of technology .tmetodq ui ۱. type of data relational data tables transaction legacy data text data web semi structured data xml graph data social network semantic web rdf … streaming data you can only scan the data once what to do with these data aggregation and statistics data warehouse and olap indexing searching and querying keyword based search pattern matching xml rdf knowledge discovery data mining statistical modeling statistics ۱ ۱ random sample and statistics population is used to refer to the set or universe of all entities under study. however looking at the entire population may not be feasible or may be too expensive. instead we draw a random sample from the population and compute appropriate statistics from the sample that give estimates of the corresponding population parameters of interest. statistic let si denote the random variable corresponding to data point xi then a statistic ˆθ is a function ˆθ s۱ s۲ · · · sn → r. if we use the value of a statistic to estimate a population parameter this value is called a point estimate of the parameter and the statistic is called as an estimator of the parameter. empirical cumulative distribution function where inverse cumulative distribution function example measures of central tendency mean population mean sample mean unbiased not robust measures of central tendency median population median or sample median example measures of dispersion range range not robust sensitive to extreme values sample range measures of dispersion inter quartile range inter quartile range iqr more robust sample iqr measures of dispersion variance and standard deviation standard deviation variance measures of dispersion variance and standard deviation standard deviation variance sample variance standard deviation univariate normal distribution multivariate normal distribution olap and data mining warehouse architecture ۲۳ client client warehouse source source source query analysis integration metadata ۲۳ ۲۴ star schemas a star schema is a common organization for data at a warehouse. it consists of fact table a very large accumulation of facts such as sales. often insert only. dimension tables smaller generally static information about the entities involved in the facts. ۲۴ terms fact table dimension tables measures ۲۵ ۲۵ star ۲۶ ۲۶ cube ۲۷ fact table view multi dimensional cube dimensions ۲ ۲۷ ۳ d cube ۲۸ day ۲ day ۱ dimensions ۳ multi dimensional cube fact table view ۲۸ rolap vs. molap rolap relational on line analytical processing molap multi dimensional on line analytical processing ۲۹ ۲۹ aggregates ۳ add up amounts for day ۱ in sql select sum amt from sale where date ۱ ۸۱ ۳ aggregates ۳۱ add up amounts by day in sql select date sum amt from sale group by date ۳۱ another example ۳۲ add up amounts by day product in sql select date sum amt from sale group by date prodid drill down rollup ۳۲ aggregates operators sum count max min median ave having clause using dimension hierarchy average by region within store maximum by month within date ۳۳ ۳۳ what is data mining discovery of useful possibly unexpected patterns in data non trivial extraction of implicit previously unknown and potentially useful information from data exploration analysis by automatic or semi automatic means of large quantities of data in order to discover meaningful patterns ۳۴ data mining tasks classification predictive clustering descriptive association rule discovery descriptive sequential pattern discovery descriptive regression predictive deviation detection predictive collaborative filter predictive ۳۵ classification definition given a collection of records training set each record contains a set of attributes one of the attributes is the class. find a model for class attribute as a function of the values of other attributes. goal previously unseen records should be assigned a class as accurately as possible. a test set is used to determine the accuracy of the model. usually the given data set is divided into training and test sets with training set used to build the model and test set used to validate it. ۳۶ decision trees ۳۷ example conducted survey to see what customers were interested in new model car want to select customers for advertising campaign training set clustering ۳۸ age income education k means clustering ۳۹ association rule mining ۴ transaction id customer id products bought sales records trend products p۵ p۸ often bough together trend customer ۱۲ likes product p۹ market basket data association rule discovery marketing and sales promotion let the rule discovered be bagels … potato chips potato chips as consequent can be used to determine what should be done to boost its sales. bagels in the antecedent can be used to see which products would be affected if the store discontinues selling bagels. bagels in antecedent and potato chips in consequent can be used to see what products should be sold with bagels to promote sale of potato chips supermarket shelf management. inventory managemnt ۴۱ if a customer buys diaper and milk then he is very likely to buy beer. so don’t be surprised if you find six packs stacked next to diapers collaborative filtering goal predict what movies books … a person may be interested in on the basis of past preferences of the person other people with similar past preferences the preferences of such people for a new movie book … one approach based on repeated clustering cluster people on the basis of preferences for …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: غروب کردن (set), نمونه برداشتن (sample), فروش (sale), جمعیت (population), خطوه (measure), جدول (table), واقعیت (fact), حاصل ضرب (product), تاریخ گزاشتن (date), ضلال (deviation),

این فایل پاورپوینت شامل ۴۷  اسلاید و به زبان انگلیسی و حجم آن ۲٫۴۴ مگا بایت است. نوع قالب فایل pptx بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ ۲۰۱۸/۱۱/۰۲ ۰۲:۲۷:۴۹ استخراج شده است.

http://www.cs.kent.edu/~jin/Cloud12Spring/BigData.pptx

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